Coping with Complexity in Knowledge Management
Knowledge management systems have experienced increased complexity due largely to the exponential growth in information technology. The increased availability of information and increased communication among stakeholders are central aspects of the formation of complex knowledge management systems. These systems are multifaceted and consist of both implicit and explicit components.
Coping with this increased complexity requires effectively dealing with the knowledge acquisition process and the knowledge representation approaches necessary in transforming individual mental models into computational models and collective conceptual models that form the basis of an effective knowledge management system.
This project builds on knowledge acquisition techniques and approaches developed in the study of expert systems, systems management and cognitive science. In particular, it draws on previous work by the authors involving knowledge acquisition in system dynamics, intelligent tutoring systems and software development projects. Knowledge elicitation, and group techniques are the primary focus since they facilitate the progression from individual mental models to collective conceptual and computer based models. The identification of implicit knowledge management components and implicit knowledge is the most serious challenge to knowledge acquisition processes.
Knowledge representations render complex knowledge repository problems manageable by the appropriate stakeholders. An ontology is the natural extension of a knowledge representation . It allows a more broadly shared
formal conceptualization of a particular domain. Ontologies allow both computer agents to navigate knowledge repositories and humans to confront knowledge management system complexity. A major issue concerning ontology development is Rhow broad of a domain should the ontology address? Different ontological paradigms are examined. They are approached as two broad categories : 1) compositional and static relationships and 2)
dynamic and causal relationships. These two broad categories mirror the structural and temporal complexity of knowledge management systems. Structural paradigms examined include object oriented languages, semantic networks and frames. Dynamic system paradigms include event-based dynamics, process-based dynamics, dialectical change and system dynamics.
The general knowledge management system under study is the University academic system. The particular system under study is academic knowledge at Howard University. This effort is part of a larger project that involves the study of intelligent knowledge engineering and management.
This paper will report on the application of select knowledge acquisition techniques, within the Systems and Computer science Department at Howard University, to determine the best ontological approaches and artifacts to
navigate the complexity of the departmentUs knowledge management system.